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1,751 results for “Transmission”
Data for "Measurement of the atom-surface van der Waals interaction by transmission spectroscopy in a wedged nano-cell"
<p>The data presented in publication <a href="http://arxiv.org/abs/1905.02783">"Measurement of the atom-surface van der Waals interaction by transmission spectroscopy in a wedged nano-cell"</a> . Published version: <a href="https://doi.org/10.1103/PhysRevA.100.022503">https://doi.org/10.1103/PhysRevA.100.022503</a></p> <p>The data are in HDF5 format, with associated metadata.</p> <p>To see examples of how to use the data, and the theoretical model for analysis, see <a href="https://github.com/thermal-vapours/TAS-Transmission-Atom-Surface">https://github.com/thermal-vapours/TAS-Transmission-Atom-Surface </a></p>
Dataset of "Smart Grids Transmission Network Testbed: Design, Deployment, and Beyond"
<p>Our test environment incorporates a unique blend of physical, emulated, and virtualized<br>components, spanning from electrical substations to SCADA systems,<br>thereby offering a versatile platform for testing against cyber threats, facilitating<br>educational programs, and supporting advanced traffic simulation. Key findings<br>from our deployment highlight the testbed’s effectiveness in identifying vulnerabilities,<br>enhancing cybersecurity measures, and providing valuable hands-on<br>learning experiences. The integration of such diverse components not only exemplifies<br>a significant step forward in testbed design but also showcases its potential<br>in fostering innovation and security in the power sector. Through detailed comparisons<br>with existing testbeds, we underscore our testbed’s distinct features<br>and its contribution to bridging the gap in current methodologies, setting a new<br>benchmark for future developments in smart grid testing and education.</p>
Florida Bay, South Florida (FCE) Seagrass Epiphyte Light Transmission from December 2000 to February 2002
Total epiphyte loads and epiphyte light transmissions were measured on Mylar seagrass leaf mimics located on Molasses Reef and the FCE Florida Bay research sites within Everglades National Park.
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
Thermally switchable, bifunctional, scalable, mid-infrared metasurfaces with VO2 grids capable of versatile polarization manipulation and asymmetric transmission
<p>The data generated by CST Studio Suite that are used to plot a part of the figures, and sample CST scripts. </p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413. </p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Croatia
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2022_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2021_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2020_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2019_HR: Ministry of Agriculture (MPS)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Austria
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2022_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2021_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2020_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2019_AT: Austrian Agency for Health and Food Safety (AGES)</li> </ul>
Short-Exposure Transmission Electron Microscopy of Cilia
<p>Noisy and pseudo ground-truth short-exposure transmission electron microscopy (TEM) images of Cilia used to obtain the results depicted in Fig. 3 in the paper "Zero-Shot Denoising of Microscopy Images Recorded at High-Resolution Limits" (Salwig & Drefs et al., 2024). The images were derived based on a dataset provided upon personal communication with the authors of the paper "Denoising of Short Exposure Transmission Electron Microscopy Images For Ultrastructural Enhancement" (Bajíc et al., 2018). </p> <p>The original dataset consisted of a sequence of 100 noisy short-exposure TEM images of a scene showing a cilium. The images had a resolution of 2048 × 2048 pixels, and each image depicted a slightly shifted version of the scene. The file pseudo-ground-truth.tif was obtained by first aligning all images of the sequence using rigid registration (Marstal et al., 2016) and subsequently computing the pixel-wise median (following a procedure discussed in Bajíc et al., 2018). The file noisy.tif was obtained by randomly selecting one image from the sequence (the 91st image).</p> <p>The images are stored in 16 bit TIF format. For visualization, use an image viewer capable of reading 16 bit images (e.g. ImageJ).</p>
SoA of measuring devices installed in NG transmission and distribution networks
<p>Deliverable D1.1 aims to design the state of the art of measuring devices in natural gas transmission and distribution networks. </p> <p>Transporting green hydrogen into existing gas assets requires carefully assessing its effect on the existing components. Since several projects have already been completed or have planned research activities to answer still-existing technical questions, the THOTH2 project focuses on the existing measuring devices. Specifically, the focus of the project regards the identification of the existing gaps in normative standards and the suggestions for solutions to cover them (if any). To contribute the hydrogen readiness of the existing gas transport and distribution infrastructures, new methodologies and protocols have to be developed to perform validated tests for metering devices. Suggestions on the need to change the standards or develop new ones will be based on the results of these experimental tests. Despite the simplicity of the methodological approach, it would be very critical when applying it to measuring devices. Several technologies are available in the market to measure gas properties. Furthermore, the operators can select more than one configuration based on the expected field conditions.</p> <p>Since limited resources are available, testing all the possible configurations would be impossible. Prioritization is required. Task 1.1 aims to collect all the information to provide a clear overview of the measuring devices installed in the existing gas assets. Specifically, this document includes the state of the art of measuring devices installed in gas assets. Different technologies are available to measure gas parameters. For example, turbine, rotary piston, ultrasonic, diaphragm, thermal mass, orifice, and Coriolis meters are available to measure flow rate. These technologies differ not only for the operating principle but also for the material used, the size available on the market, and the effect that different conditions could have on the metrological performances like, for example, overload conditions, flow rate pulsations, leakages through the clearance and pressure drops. Furthermore, different maintenance activities are usually expected, resulting in different operative costs throughout the lifetime. To date, turbine, rotary piston gas, and ultrasonic meters are used for fiscal gas metering in transmission networks. Specifically, based on the data collected, turbine gas meters are the most installed technologies for medium to high flow rate, followed by rotary piston and ultrasonic (for high flow rate). Few cases of use of Coriolis meters have been found. Regarding distribution, a different situation results. Despite the fact that few answers have been received to date, and only from Italy, it appears that diaphragm gas meters are the prevailing technology installed, even if a greater penetration is expected for thermal mass meters. THOTH2 also includes other measurements like gas quality by chromatographs, pressure and temperature, and trace water dew point. Regarding temperature, it was assumed that since the sensor is not in contact with the fluid but is protected by the thermowell, it can be assumed that no problem would arise. However, further investigation should be performed to investigate if any effect of hydrogen on response time exists. Regarding pressure measurement, many models are commercially available, but attention should be given to the effect of hydrogen on the material with which the fluid is in contact. Specifically, identifying critical materials that can be affected by hydrogen among those available in commercial products should be the next step to identifying the products to be tested. Gas chromatographs are also present in different models and configurations in the existing networks. Usually, different columns are used based on the specific analysis to be performed. Even if the range of the concentration allowed for each molecule is usually known for each model, more details about the configuration of each gas chromatograph are needed to complete the analysis and check the capability to handle hydrogen. Only some models of trace water sensors have been identified in the investigated networks. Specifically, impedance sensors result in the most implemented devices. Other devices are also typically used in the networks. Electronic Volume Converters and Flow Computers convert measurements into standardized gas volumes for fiscal purposes. The main issues to be investigated are the implemented algorithms and their capability to consider hydrogen. The main algorithms are AGA8, SGERG, and AGA-NX19, and the Operators can check the hydrogen limits. The main issue is that many different models are installed in gas transmission and distribution networks. Furthermore, based on the conclusion about pressure and temperature sensors, the potential effects of hydrogen on the metrological performances of those devices that have these sensors integrated have to be carefully assessed not to overcome the limits on errors provided by the standards. Last, leak detection is essential to detect fugitive emissions to the atmosphere and to minimize the risk of failures or accidents . To date, many devices are supplied to the technicians on the field to verify the presence of hazardous substances. Since different sensors can be implemented in the same devices to measure different quantities, attention should be given in Task 2.1 to selecting those sensors that, on the current knowledge, appear to be most critical when being in contact with hydrogen.</p>
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Phylogenetic and epidemiologic data relating to age-specific HIV incidence and transmission in Rakai, Uganda, 2003-2018.
<p>This repository contains the data for the analyses presented in the paper Growing gender inequity in HIV infection in Africa: sources and policy implications by M. Monod, A. Brizzi, R. Galiwango, R. Ssekubugu, Y. Chen, X. Xi et al. available in the pre-print <a href="https://doi.org/10.1101/2023.03.16.23287351">https://doi.org/10.1101/2023.03.16.23287351</a> </p> <p>We thank all contributors, program staff and participants to the Rakai Community Cohort Study; all members of the PANGEA-HIV consortium, the <a href="https://www.rhsp.org/index.php">Rakai Health Sciences Program</a>, and CDC Uganda for comments on an earlier version of the manuscript.</p> <p>We also extend our gratitude to the <a href="https://doi.org/10.14469/hpc/2232">Imperial College Research Computing Service</a> and the <a href="https://www.bdi.ox.ac.uk/about/biomedical-research-computing">Biomedical Research Computing Cluster</a> at the University of Oxford for providing the computational resources to perform this study. Additionally, we thank the Office of Cyberinfrastructure and Computational Biology at the <a href="https://www.niaid.nih.gov/">National Institute for Allergy and Infectious Diseases</a> for data management support; and Zulip for sponsoring team communications through the Zulip Cloud Standard chat app. </p> <p>All analysis code is available from <a href="https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender">https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender</a>.</p>
PanTaGruEl - a pan-European transmission grid and electricity generation model
<p>If you have any questions or comments, please write to <a href="mailto:laurent.vincent.pagnier@gmail.com">laurent.vincent.pagnier@gmail.com</a>.</p> <p>When publishing results based on this data set, please cite:</p> <p>L. Pagnier, P. Jacquod, “Inertia location and slow network modes determine disturbance propagation in large-scale power grids”, PLOS ONE 14(3): e0213550, 2019. <a href="https://doi.org/10.1371/journal.pone.0213550">PLOS ONE 14(3): e0213550</a>, 2019.</p> <p>and</p> <p>M. Tyloo, L. Pagnier, P. Jacquod, “The Key Player Problem in Complex Oscillator Networks and Electric Power Grids: Resistance Centralities Identify Local Vulnerabilities”, <a href="https://doi.org/10.1126/sciadv.aaw8359">Science Advances 5(11): eaaw8359</a>, 2019.</p> <p><strong>Description:</strong></p> <p>PanTaGruEl is a dynamical grid model designed to investigate the propagation of disturbances in the continental European transmission grid.</p> <p>The construction of the model is detailed <a href="https://doi.org/10.1371/journal.pone.0213550.s002">here</a>.</p> <p><strong>Features</strong>:</p> <ul> <li>Precise distribution of national demands to network buses.</li> <li>Realistic electrical parameters of transmission lines.</li> <li>Merit-Order based economic dispatch of generators.</li> <li>Dynamical parameters of generators and loads for transient stability investigations.</li> </ul> <p><strong>Files:</strong></p> <p>Data files:</p> <p>Our model is provided in an extended Matpower format and as csv raw data. For more information on Matpower format, see Appendix B of its <a href="https://matpower.org/docs/MATPOWER-manual.pdf">manual</a>.</p> <p>Script files:</p> <p><em>opf_ex.m </em>performs optimal power flow computations for two load configurations.<br> <em>spectral_ex.m</em> presents a basic spectral analysis.<br> <em>dynamics</em><em>_ex.m</em> give a minimal example of dynamical simulations.</p> <p><strong>Requirements:</strong></p> <p>Our model has been developed for use with <a href="https://matpower.org/">Matpower</a>. If you are interested in a port to another language, please <a href="mailto:laurent.vincent.pagnier@gmail.com?subject=Info%20on%20PanTaGruEl">contact us</a>.</p> <p><strong>Acknowledgement:</strong></p> <p>The authors thank M. Tyloo and K. Van Walstijn for their useful comments and remarks on the model.</p> <p><strong>Sources</strong>:</p> <p>B. Wiegmans, <a href="https://doi.org/10.5281/zenodo.55853">“GridKit extract of ENTSO-E interactive map”</a><br> Global Energy Observatory, <a href="http://globalenergyobservatory.org">“GEO Power plants database”</a><br> Siemens, <a href="http://siemens.com/power-engineering-guide">“Power Engineering Guide”</a></p>
Simulated calibration dataset for 4D scanning transmission electron microscopy
<p>4D-STEM data frequently requires a number of calibrations in order to make accurate measurement: for instance, in various cases, it can be essential to measure and correct for diffraction shifts, account for ellipticity in the diffraction patterns, or determine the rotational offset between the real and diffraction planes.</p> <p>We've prepared a simulated 4D-STEM dataset which includes diffraction shifting, elliptical distortion, and an r-space/k-space rotational offset. Two HDF5 files each include the simulated data for two different electron probes: a standard probe, using a circular probe-forming aperture, and a 'bullseye' probe, using a patterned aperture. Each HDF5 file contains the following data objects:</p> <p>(a) the 'experimental' 4D-STEM scan of a strained single-crystal gold nanoparticle (size: (100,84,250,250) )</p> <p>(b) a 4D-STEM scan of a calibration sample of polycrystalline gold (size: (100,84,250,250) )</p> <p>(c) a stack of diffraction images of the electron probe over vacuum (size: (250,250,20) )</p> <p>(d) a single image of the electron probe over the sample and far from focus, such that the CBED forms a shadow image (size: (512,512) )</p>
Data and code for the analysis in "Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland"
<p>Data and code used for the analysis in <em>Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland</em> (Lemaitre et al., Swiss Medial Weekly 2020).</p>
Synchronous transmissions on Bluetooth 5 and IEEE 802.15.4 - A replication study
<p>Archive of the following GitHub repository: <a href="https://github.com/romain-jacob/ST_data_and_viz">https://github.com/romain-jacob/ST_data_and_viz</a></p> <p>This repository contains the raw data and analysis scripts of a replication study of synchronous transmissions using the <a href="https://www.nordicsemi.com/en/Software%20and%20tools/Development%20Kits/nRF52840%20Dongle">nRF52840 Dongle</a> where we compare the success rate of synchronous transmission attempts using two transmitters while varying a number of parameters. The repository also contains the source code of a data visualization application, which you can either</p> <ul> <li>run locally or</li> <li><a href="https://github.com/romain-jacob/ST_data_and_viz/blob/master/URL">online in your web browser</a> at <a href="http://explore-st-data.ethz.ch/">http://explore-st-data.ethz.ch/</a></li> </ul> <p>The study is described in more details in the following publication.</p> <blockquote> <p><strong>Synchronous transmissions on Bluetooth 5 and IEEE 802.15.4 - A replication study</strong><br> Romain Jacob, Anna-Brit Schaper, Andreas Biri, Reto da Forno, Lothar Thiele<br> <a href="https://cpsbench20.ethz.ch/">CPS-IoTBench'20</a><br> [ <a href="https://openreview.net/forum?id=BSZPNEUHiS2">Direct link</a> ]</p> </blockquote> <p>The dataset has been collected during the <a href="https://doi.org/10.3929/ethz-b-000375332">Master thesis of Anna-Brit Schaper.</a></p>
Phenotypic data related to genetic architecture of transmission stage production and virulence in schistosome parasites
<p>These data were generated related to the study of the <strong>Genetic architecture of transmission stage production and virulence in schistosome parasites</strong>.</p> <p><strong>Abstract:</strong> Both theory and experimental data from multiple pathogens suggest that the production of transmission stages should be strongly associated with virulence, but the genetic bases of parasite transmission/virulence traits are poorly understood. In the blood fluke <em>Schistosoma mansoni</em>, parasite genotypes show extensive variation in numbers of cercariae larvae shed from infected snails. Furthermore, high shedding parasites cause high mortality to snails while low shedding parasites cause low mortality, consistent with expected trade-offs between parasite transmission and virulence. To understand the genetic basis of transmission stage production/virulence, we conducted reciprocal crosses between schistosomes from two laboratory populations that differ 8-fold in cercarial shedding and in their virulence to inbred snail hosts. Each parasite generation, we determined four-week cercarial shedding profiles in inbred <em>Biomphalaria glabrata</em> snails infected with single parasite larvae. We sequenced the whole genome of the F0 parents and the exome of the F1 progeny and 188 F2 progeny from each cross, and used linkage mapping to reveal quantitative trait loci (QTLs) underlying transmission stage production. Cercarial production is polygenic: we found three major QTLs on chromosome 1, 3 and 5 (Log-of-the-odds (LOD) = 5.61, 8.19, 6.25) and two minor QTLs on chromosome 2 and 4. These QTLs act additively and explained 28.56% of the phenotypic variation in cercarial shedding. Alleles inherited from the high and low shedding parents were co-dominant at all QTLs, except for chr. 1 and chr. 4 where the “high cercarial shedding” allele is recessive. These results demonstrate that the genetic architecture of key traits directly relevant to schistosome ecology can be dissected using classical linkage mapping approaches, and set the stage for fine mapping and functional validation of the genes involved using the growing armory of functional and cell biology tools available for this parasite.</p> <p> </p> <p>This dataset is made of 4 tables:</p> <ul> <li>F0_parental_populations.csv</li> <li>F1.csv</li> <li>F2.csv</li> <li>sex.tsv</li> </ul> <p> </p> <p><strong>F0_parental_populations.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of <em>Schistosoma mansoni</em> parasite. We have compared the transmission stage production between two different populations of <em>S. mansoni</em> parasite. This dataset was originally published in Le Clec'h et al., 2019 (Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasites and Vectors. 2019 Oct 16;12(1):485. doi: 10.1186/s13071-019-3741-z).</p> <p> </p> <p>This table is made of 9 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>schistosoma_population</strong>: the population of schistosome used for the infection of the snail. Each snail was infected with a single parasite genotype. We have used SmLE (high shedder/highly virulent population) and SmBRE (low shedding/low virulent population).</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F1.csv</strong></p> <p> </p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F1 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 11 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F1A or F1B cross. Each snail was infected with a single parasite genotype from either F1A or F1B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4).</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined by PCR <sup>1</sup>.</li> </ul> <p> </p> <p><strong>F2.csv</strong></p> <p>This table contains the number of cercariae produced by each individual <em>Biomphalaria glabrata</em> Bg26 snails infected with single genotypes of F2 progeny from the cross SmLE x SmBRE (see the manuscript for details).</p> <p> </p> <p>This table is made of 10 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample.</li> <li><strong>cross</strong>: F2A or F2B cross. Each snail was infected with a single parasite genotype from either F2A or F2B progeny.</li> <li><strong>Shed.1</strong>: the number of cercariae produced by each parasite genotype at the first shedding week (4 weeks after exposure to parasite).</li> <li><strong>Shed.2</strong>: the number of cercariae produced by each parasite genotype at the second shedding week (5 weeks after exposure to parasite).</li> <li><strong>Shed.3</strong>: the number of cercariae produced by each parasite genotype at the third shedding week (6 weeks after exposure to parasite).</li> <li><strong>Shed.4</strong>: the number of cercariae produced by each parasite genotype at the fourth shedding week (7 weeks after exposure to parasite).</li> <li><strong>sum</strong>: the sum of the cercariae produced by each parasite genotype over the 4 weeks of shedding (Shed.1 + Shed.2 + Shed.3 + Shed.4)</li> <li><strong>average</strong>: the average number of cercariae produced by each parasite genotype over the 4 weeks of shedding.</li> <li><strong>PO</strong>: the total phenoloxidase activity in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>2</sup>.</li> <li><strong>Hb</strong>: the hemoglobin rate in infected snail hemolymph, measured at 7.5 weeks post-exposure <sup>3</sup>.</li> </ul> <p> </p> <p><strong>sex.csv</strong></p> <p> </p> <p>This table contains the <em>in silico</em> sexing of F0 parents, F1 parents and F2 progeny of <em>S. mansoni</em> parasites.</p> <p>This table is made of 4 columns:</p> <ul> <li><strong>id</strong>: the unique identifier of each sample</li> <li><strong>read_depth</strong>: the read depth ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>ratio</strong>: computed ratio between the Z-linked and pseudo-autosomal regions.</li> <li><strong>sex</strong>: the sex of each parasite genotype determined <em>in silico</em>: a ratio around 1 corresponds to a male carrying two Z chromosomes while a ratio around 0.5 corresponds to a female carrying only one Z chromosome.</li> </ul> <p><strong>Notes:</strong></p> <p><sup>1</sup>. Le Clec’h W, Chevalier F et al. Real-time PCR for sexing Schistosoma mansoni cercariae. Mol Biochem Parasitol. Jan-Feb 2016; 205(1-2):35-8.doi: 10.1016/j.molbiopara.2016.03.010. Epub 2016 Mar 26.</p> <p><sup>2</sup>. Le Clec’h W et al. Characterization of hemolymph phenoloxidase activity in two Biomphalaria snail species and impact of Schistosoma mansoni infection. Parasit Vectors. 2016 Jan 22; 9:32.doi: 10.1186/s13071-016-1319-6.</p> <p><sup>3</sup>. Le Clec'h et al. Striking differences in virulence, transmission and sporocyst growth dynamics between two schistosome populations. Parasit Vectors. 2019 Oct 16; 12(1):485. doi: 10.1186/s13071-019-3741-z.</p>
Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the entire ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a> since git is not suited for handling large changing files. Instead, we provide separate data bundles and cutouts to be downloaded and extracted, as noted in the documentation.</p> <p>The provided <strong>cutouts </strong>are merged spatiotemporal subsets of the European weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V003">CMSAF SARAH-3</a> solar surface radiation dataset for the years 1996, 2010, 2012, 2013, 2019, 2020 and 2023. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p>Solar irradiation data is taken from SARAH-3 while all other weather data is from ERA5.</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul> <p><strong>CMSAF SARAH-3</strong></p> <ul> <li>Pfeifroth, Uwe; Kothe, Steffen; Drücke, Jaqueline; Trentmann, Jörg; Schröder, Marc; Selbach, Nathalie; Hollmann, Rainer (2023): Surface Radiation Data Set - Heliosat (SARAH) - Edition 3, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V003, <a href="https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003" target="_blank" rel="noopener">https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003</a>.</li> <li><strong>Terms of Use:</strong> All intellectual property rights of the CM SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.</li> </ul>
Global Transmission Database
<p>The Global Transmission Database (GTD) consists of comprehensive data regarding existing and planned cross-border transmission capacities globally collated from public sources. The dataset is oriented towards representing entry level capacity data (MW) that can be used in energy system models and other computational tools. Transmission capacities are provided at a country-to-country basis in addition to regional level data for a sample of larger countries (Australia, Brazil, Canada, China, India, Indonesia, Japan, Philippines, Russian Federation, United States, Vietnam). Capacities are provided for land-based transmission pathways as well as for subsea pathways. Refer to the accompanying paper for details on the applied methodology as well as a file by file description of the repository content.</p>
Data and model for 'JWST transmission spectroscopy of HD 209458b: a super-solar metallicity, a very low C/O, and no evidence of CH4, HCN, or C2H2'
<p>Supplementary materials for https://arxiv.org/abs/2310.03245 </p> <p>include:</p> <p>1. <strong>spectra_final.csv: </strong>transmission spectrum reduced by Eureka! and SPARTA (Figure 6), the best-fit model presented in Figure 1(a).</p> <p>2. <strong>Opacities </strong>used in the retrieval that are compatible with PLATON described in section 3.</p> <p>All opacity numpy pickle files are generated by <code>Python 3.9.7</code> and <code>Numpy 1.24.2</code>.</p> <p> </p> <p>**Bestfit in spectra_final.csv and all opacities are updated on Jan 23, 2024</p> <p>For any additional data requests or questions, please contact: qiaox@uchicago.edu</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.